Top Machine Learning Jobs in Paris

Design and deploy AI agents and automation for legal workflows, advise on AI tool procurement and responsible use, and lead legal operations transformation. Optimize matter management, vendor oversight, budgeting, CLM systems, and performance reporting while partnering cross-functionally. Provide legal guidance on technology, privacy, data protection, regulatory compliance, and intellectual property, and train legal, compliance, and business teams on AI-related topics.

Lead end-to-end Celonis implementations for strategic customers, managing scope, timelines, resources, quality, governance, risks, and value realization. Support pre-sales proposals, manage executive and operational client relationships, coordinate cross-functional and distributed teams, promote AI-enabled delivery, mentor Services team members, and improve implementation methods. The role requires strong project leadership, communication, analytical problem-solving, and experience delivering complex business and technical projects in consulting or professional services.

Develop and deploy AI, LLM, and generative AI agent tools for credit trading workflows. Apply machine learning to trading signals, portfolio optimization, risk monitoring, hedging, and automated reporting. Build trade-support infrastructure, analyze credit portfolio data, collaborate with model control teams, and advance automation across credit portfolio lending markets.

Conduct product-focused research on machine learning for wearable-device time-series data. Design experiments, build signal-processing and ML pipelines, and develop cognitive and physiological decoding models from multimodal biological signals. Collaborate with hardware, software, and research teams on data capture, model evaluation, and demos, while translating findings into recommendations for leadership. The role requires strong biosignal processing, Python, wearable biometrics, and applied research or engineering experience.

Leads global go-to-market strategy and commercial execution for IFS’s AI Optimization portfolio, including PSO, MSO, and WISE. Owns portfolio positioning, proposition development, sales plays, pipeline growth, strategic deal support, and revenue enablement. Partners with Product, Sales, Marketing, Customer Success, and Partner teams to improve market penetration, win rates, forecasting, and customer adoption while translating complex AI and optimization capabilities into compelling commercial narratives.

Designs, develops, deploys, and monitors machine learning, deep learning, generative AI, LLM, RAG, and agentic AI solutions. The role covers predictive modeling, feature engineering, experimentation, MLOps, LLMOps, explainability, model optimization, and AI pipeline integration with enterprise systems. The Data Scientist collaborates with engineering and business teams, promotes responsible AI practices, evaluates emerging technologies, and serves as a technical reference for data science and AI engineering.

Leads global epidemiology and real-world evidence strategy for oncology products across pre- and post-launch development. Designs and oversees prospective and retrospective studies, integrated evidence plans, regulatory submissions, payer and HTA evidence, and advanced analytics. Partners with Medical Affairs, Clinical Development, Regulatory, Market Access, HEOR, Biostatistics, vendors, and external experts while ensuring scientific rigor, compliance, quality, budgets, and timelines. The role also provides methodological leadership in patient-reported outcomes, psychometrics, and clinically meaningful change.

Develop quantitative models and macroeconomic and financial scenarios for budgeting, risk management, and regulatory stress testing. Build forecasting, econometric, machine-learning, uncertainty-quantification, and scenario-analysis solutions using large datasets and AWS cloud infrastructure. Collaborate with Finance, Risk, and business stakeholders to translate requirements into decision-ready analysis, while supporting model validation, governance, regulatory documentation, deployment, and monitoring.

Lead end-to-end data and AI architecture for enterprise client engagements, covering cloud infrastructure, data pipelines, machine learning, generative AI, and agent-based systems. Serve as the primary technical advisor to senior stakeholders, guide architecture reviews, ensure production readiness, and resolve engineering impediments. Support pre-sales through technical scoping, proposals, estimates, and client presentations. Mentor consultants and engineers while developing reusable AI accelerators and reference architectures.

As an AI Researcher at Datadog, you'll conduct cutting-edge research in AI, focusing on foundation models and AI agents for observability and reliability, collaborating with cross-functional teams, and contributing to scientific publications.

Supports short-term optimization of EDF’s power asset portfolio across intraday, day-ahead, and week-ahead markets. Responsibilities include real-time risk analysis, forecasting flexibility and production costs, communicating time-sensitive information to traders, ensuring regulatory compliance, and optimizing OTC and EPEX positions. The role also develops analytical tools and forecasting models using Python, JavaScript, and occasionally Rust, while maintaining data quality and availability.

Leads country-level automation, analytics, and AI initiatives supporting clinical trial operations. Responsibilities include identifying opportunities, developing business cases, translating requirements into solutions, overseeing dashboards and workflow automation, managing testing and deployment, tracking KPIs, ensuring ICH GCP and regulatory compliance, and coordinating cross-functional stakeholders. The role also supports solution governance, lifecycle management, regional scaling, change enablement, and continuous improvement.

Intern supporting Global Regulatory CMC through data analytics, visualization, regulatory intelligence, AI and predictive analytics. Responsibilities include developing SharePoint knowledge platforms, building Power BI and Excel reporting tools, improving data quality and governance, automating regulatory processes, supporting Veeva digitalization, analyzing Health Authority questions, and contributing to forecasting and submission-readiness initiatives.

Research data-centric methods that improve humanoid robot learning. Responsibilities include designing multimodal datasets, data curation and selection tools, training-stage data composition, controlled ablations, scaling studies, and real-robot evaluation. The role connects research findings to data collection strategy and reproducible training workflows, collaborating with AI, robotics, infrastructure, and data engineering teams.

Develops data analytics, data science, and AI solutions for internal audit, including pipelines, automations, APIs, machine-learning analyses, and reusable tools. Translates audit and risk questions into reliable, evidence-based insights; strengthens data governance and code quality; and trains auditors through coaching and bootcamps. The role also drives the internal audit data and AI roadmap, supports technology innovation, and collaborates internationally across Ayvens and Societe Generale.

Build and deploy data processing pipelines for fraud detection and AI products. Conduct R&D in machine learning, NLP, image analysis, trend analysis, and chatbots. Collaborate with clients, support prospect demonstrations, incorporate feedback, and contribute across the complete data science product lifecycle. The six-month internship requires a master’s student with production coding experience, fluent French, English communication skills, and an internship agreement.

Design, build, evaluate, deploy, and scale production AI systems for enterprise and digital-native customers. Responsibilities include architecture, coding, debugging, integrations, evaluation harnesses, reliability, observability, security, governance, and optimization of latency and cost. The role partners with customer and internal teams to move AI prototypes into reliable production systems, drive adoption, create reusable deployment tools and patterns, and translate customer deployment experience into product feedback.

Own a large portfolio of mid-market B2B accounts, driving renewals, retention, expansion, and revenue growth. Use usage data, health scores, and account signals to prioritize engagement, mitigate churn, identify upsell opportunities, and maintain accurate CRM forecasts. Contribute to scalable adoption initiatives, webinars, process improvements, and cross-functional collaboration with Support and Sales. The role requires fluent French and English, analytical account planning, negotiation, and operational rigor.

Own the AI Potluck contributor roadmap, assess partner contributions, write product requirements, and guide integrations from initial concept through launch. Manage technical partner relationships, develop repeatable contribution pathways, align external integrations with product and engineering roadmaps, and drive ecosystem adoption. Collaborate with Product, Engineering, Finance, Research, Programmes, and Movement teams to advance open-source, public-interest AI products.

Lead the development of virtual pharmacology models using complex biological datasets. Assess data and proposals, build pipelines and machine learning proof-of-concepts, identify data gaps, collaborate with laboratory scientists, establish scientific validation, and prepare models for internal use and potential commercialization. Ensure reproducible, documented code and communicate findings to scientific, technical, commercial, and non-specialist audiences. The role may later help build and lead a data science team.

Evaluate and improve Fin, Back Market’s AI customer care agent, by reviewing conversations, monitoring quality scorecards, identifying defects and root causes, analyzing performance across markets and use cases, and tracking corrective actions. The intern will document evaluation frameworks and provide actionable feedback to product, engineering, content, and data teams.

Develop and implement machine learning and deep learning algorithms to analyze MRI data, collaborate with neuroscientists and clinicians to define model requirements, evaluate and optimize models for clinical applications, and write reports and scientific publications.

Develop and enable VAR and reseller partners across EMEA South by providing technical presales support, architectural guidance, solution positioning, workshops, and sales-cycle expertise. Build joint offerings, cultivate technical champions, present to executive stakeholders, and collaborate with sales, marketing, product, and partner teams. The role requires expertise in data platforms, pipelines, cloud infrastructure, DevOps, CI/CD, and ideally machine learning, with up to 25% travel.

Six-month Data and AI internship contributing to machine learning, optimization, generative AI, LLM pipelines, RAG, agentic workflows, and model production practices. Responsibilities include developing algorithms, retraining and monitoring models, evaluating AI quality and robustness, documenting failures, supporting responsible AI compliance, and explaining concepts to deployment managers. The role requires Python, data science and machine learning knowledge, data management experience, analytical ability, fluent English, and a university internship agreement.

Develop computer vision and machine learning algorithms for robotics and aerospace projects. Responsibilities include researching vision methods, preparing datasets, building training pipelines, optimizing models, deploying solutions, validating performance in real-world environments, monitoring production systems, and collaborating with software and simulation teams.